CN110500083A - A kind of oil-water well dynamic connectivity method of discrimination - Google Patents
A kind of oil-water well dynamic connectivity method of discrimination Download PDFInfo
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- CN110500083A CN110500083A CN201910716460.7A CN201910716460A CN110500083A CN 110500083 A CN110500083 A CN 110500083A CN 201910716460 A CN201910716460 A CN 201910716460A CN 110500083 A CN110500083 A CN 110500083A
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
- E21B43/16—Enhanced recovery methods for obtaining hydrocarbons
- E21B43/20—Displacing by water
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
Abstract
The invention belongs to oil field development technical field more particularly to a kind of oil-water well dynamic connectivity method of discrimination.The present invention determines the connectivity between oil-water well by differentiating the degree of association size between oil well and well two subsystems.If oil well is consistent with well variation tendency, i.e., synchronous variation degree is higher, it is believed that the two association is larger, and connectivity is preferable；Conversely, the two degree of association is smaller, connectivity is poor.The analysis method related coefficient has quantified grease inter well connectivity development trend process.By the multiple related coefficients of comprehensive analysis, the present invention by qualitatively judge whether advantageous channel, can classify to injection-production well, provide guidance for next step corrective measure.
Description
Technical field
The invention belongs to oil field development technical field more particularly to a kind of oil-water well dynamic connectivity method of discrimination.
Background technique
Currently, domestic majority oil field is all made of waterflooding extraction, stratum energy is supplemented using water filling, and then oil reservoir is driven to carry out
Exploitation.Oil reservoir block water flooding recovery generallys use the process flow of " more mouthfuls of individual wells of water source-water-injection station-".Water-injection station is by water source
Water boosting, then injects individual well by pipe network.Individual well after filling the water a period of time, often will appear note not enough, note not into
Phenomenon (is known as shorting).The key for solving the problems, such as this is the dynamic connectivity between clear injection-production well, and then takes corresponding measure,
To solve to short.To determine that connectivity between injection-production well, the prior art generally use two methods:
1, conventional method
Including Interwell tracer method, method for testing pressure, well test analysis method etc., these methods need to carry out targetedly
Construction, takes a long time, influences normally to produce, and operating expenses is high.
2, inverting class method
Inverting class method is to regard the oil reservoir block of waterflooding development as a dynamic equilibrium system, injection well water injection parameter
Variation will lead to the variation of the output data of the producing well communicated therewith, it is believed that thus researcher establishes a variety of inverse models
Inverting note produces the models such as the connectivity between well, such as linear regression, nonlinear regression, sliding recurrence, correlation coefficient process.It is more straight
A kind of method connect is to calculate the strong and weak positive correlation of similarity degree and inter well connectivity that note adopts data variation rule, injects data
Grey relational grade between curve and output data curve embodies the power of inter well connectivity with the size of grey correlation angle value.
Existing method all uses Deng Shi grey relational grade formula, but what Deng Shi degree of association formula reflected by displacement difference is connecing between sequence
Nearly property more pays close attention to the absolute distance between sequence.The article that Xiao Xinping was delivered in 1997 is " about grey correlation metrization mould
The theoretical research and comment of type " it points out, nondimensionalization operation may change the relatively strong and weak sequence of inter well connectivity.2015 old
The applications such as island the patent method and device of interwell communication relationship " a kind of obtain " (application number: 201510733980.0, application
Publication No.: CN 105389467A) grey relational grade infused between producing data sequence is still calculated using Deng Shi grey relational grade formula
Value uses DWT algorithm to calculate the dynamic time similar value that note produces between data sequence and (infuses the time lag between producing data with reflection
Effect), then calculate grey correlation angle value and dynamic time similar value linear weighted function and, and think the more big then well of the weighted sum
Between connectivity it is stronger, but obtained result error is larger, cannot there is preferable guiding opinion to actual production.
Summary of the invention
The present invention provides a kind of oil-water well dynamic connectivity method of discrimination, the first purpose be to provide a kind of time it is short,
It does not affect the normal production and the lower oil-water well dynamic connectivity method of discrimination of operating expenses；The second purpose is to provide a kind of knot
Fruit is accurate, there is the oil-water well dynamic connectivity method of discrimination of preferable guiding opinion to actual production.
To achieve the above object, the technical solution adopted by the present invention is that:
A kind of oil-water well dynamic connectivity method of discrimination, includes the following steps:
Step 1: the data type that data analysis uses is determined
Determine the essential data that the monthly injection rate of well is analyzed as data；
Step 2: analytical sequence is determined
Select injection well or output well will using the centerwell data of concern as reference sequence as the centerwell of concern
Another well data are taken as comparing ordered series of numbers；
Y is set as reference sequence, i.e. Y is
Y=y (k) | k=1,2 ... n }；(1)
X is set as comparing ordered series of numbers, i.e. X is
Xi={ xi(k) | k=1,2 ... n }, i=1,2 ... m；(2)
Wherein:
Y (k) is reference sequence data element；
X (k) is to compare ordered series of numbers data element；
K is element numbers in ordered series of numbers；
I is to compare ordered series of numbers type mark number；
Step 3: the nondimensionalization of variable
By the reference sequence that step 2 determines and the data for comparing ordered series of numbers, carried out using equalization or section mode immeasurable
Guiding principle；
Step 4: calculating correlation
Weight is sought using entropy weight weighing method, and then determines the degree of association；
Step 5: relational degree taxis simultaneously judges connectivity
The association angle value that step 4 is calculated carries out size sequence, the big connectivity with the centerwell of concern of association angle value
It is better than the association small connectivity with the centerwell of concern of angle value.
Data type further includes the monthly Liquid output of oil well, chooses in two item data of monthly moisture content in the step one
It is one or two kinds of.
Nondimensionalization is carried out using equalization or section mode in the step three, a point following situation carries out:
Initial value processing: Xi/xi(1)={ x 'i(k) | k=1,2 ... n, xi}, (1) ≠ 0 i=1,2 ... m；(3)
Data are adopted for accumulation note, because data have apparent increasing trend, then use equalization nondimensionalization processing side
Formula；
Data are adopted for monthly note, because data do not have apparent increasing trend, are then handled using section nondimensionalization
Mode；
Wherein: i is to compare ordered series of numbers type mark number；
K is the ordered series of numbers element numbers after nondimensionalization；
N is element number in ordered series of numbers；
xiIt (k) is k-th of element in original data series；
The step four seeks weight using entropy weight weighing method, and then determines that the degree of association is to obtain with the following method:
It is located at m indexs, n to be evaluated in the system of object, iotave evaluation matrix is Dn,m, place is standardized to it
Reason obtains Standard Process Rn,m, according to entropy weight law theory, the entropy of jth item is calculated by following formula
The entropy weight (weight) of jth item is calculated by following formula:
In above formula:fi,jWhen=0, fi,jlnfi,j=0；
Wherein: HjFor the entropy of jth item；
I is to be evaluated object tag number；
N is to be evaluated object total number；
fi,jFor intermediate variable；
ωjFor the entropy weight of jth item.
Relating value r in the step four0,iIt is to be calculated by following formula
Wherein, i=1,2 ... m.
The utility model has the advantages that
The present invention passes through the data type for determining data analysis and using, determines analytical sequence, the nondimensionalization of variable, calculating
The degree of association and relational degree taxis simultaneously judge five steps of connectivity, provide that a kind of time is short, does not affect the normal production and constructs
The lower oil-water well dynamic connectivity method of discrimination of expense, differentiation result of the invention is accurate, has preferable finger to actual production
Lead meaning.
The above description is only an overview of the technical scheme of the present invention, in order to better understand technology hand of the invention
Section, and can be implemented in accordance with the contents of the specification, and with presently preferred embodiments of the present invention and attached drawing be cooperated to be described in detail such as below
Afterwards.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without creative efforts, can also obtain according to these attached drawings
Obtain other attached drawings.
Fig. 1 is flow chart of the invention；
Fig. 2 is the reference sequences in the embodiment of the present invention and compares sequence variation trend schematic diagram；
Fig. 3 is the distribution schematic diagram of injection well of the present invention and output well.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiment is only a part of the embodiments of the present invention, instead of all the embodiments.Base
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
His embodiment, shall fall within the protection scope of the present invention.
Embodiment one:
A kind of oil-water well dynamic connectivity method of discrimination according to figure 1, includes the following steps:
Step 1: the data type that data analysis uses is determined
Determine the essential data that the monthly injection rate of well is analyzed as data；
Step 2: analytical sequence is determined
Select injection well or output well will using the centerwell data of concern as reference sequence as the centerwell of concern
Another well data are taken as comparing ordered series of numbers；
Y is set as reference sequence, i.e. Y is
Y=y (k) | k=1,2 ... n }；(1)
X is set as comparing ordered series of numbers, i.e. X is
Xi={ xi(k) | k=1,2 ... n }, i=1,2 ... m；(2)
Wherein:
Y (k) is reference sequence data element；
X (k) is to compare ordered series of numbers data element；
K is element numbers in ordered series of numbers；
I is to compare ordered series of numbers type mark number；
Step 3: the nondimensionalization of variable
By the reference sequence that step 2 determines and the data for comparing ordered series of numbers, carried out using equalization or section mode immeasurable
Guiding principle；
Step 4: calculating correlation
Weight is sought using entropy weight weighing method, and then determines the degree of association；
Step 5: relational degree taxis simultaneously judges connectivity
The association angle value that step 4 is calculated carries out size sequence, the big connectivity with the centerwell of concern of association angle value
It is better than the association small connectivity with the centerwell of concern of angle value.
The size of the degree of association has reacted the similarity degree that note adopts data sequence, reflects inter well connectivity indirectly, therefore can
To judge inter well connectivity from the degree of association.It can be according to the content of day regular data, using single injection data and single output number
It is judged according to the degree of association of (referred to as one group of data), is capable of the degree of association of comprehensive analysis multi-group data, judge to connect between note produces well
The general character it is relatively strong and weak, and then qualitatively judge whether advantageous channel, guidance can be provided to next step augmented injection corrective measure, made
Water-injection station achievees the purpose that energy-efficient operation.
It in actual use, is the influence for overcoming the time lag between note production well, the note selected in this method produces data
Sequence using certain (such as one month, half a month, one week, etc.) period daily mean.When can overcome to a certain extent in this way
Between hysteresis influence, and convenient for operation.
The present invention is that a kind of time is short, do not affect the normal production and the lower oil-water well dynamic connectivity of operating expenses differentiates
Method, differentiation result of the invention is accurate, has preferable directive significance to actual production.
Embodiment two:
A kind of oil-water well dynamic connectivity method of discrimination according to figure 1, with embodiment one the difference is that: it is described
The step of one in data type further include the monthly Liquid output of oil well, choose in two item data of monthly moisture content it is one or two kinds of.
In actual use, the monthly injection rate of well is the data that must be used, and the data of the oil well of use are according to having
Data cases, use is one or two kinds of, can obtain satisfied effect.Because concerning oil production, for
Well data, it is preferable using monthly Liquid output effect data.
Embodiment three:
A kind of oil-water well dynamic connectivity method of discrimination according to figure 1, with embodiment one the difference is that: it is described
The step of three in nondimensionalization carried out using equalization or section mode, point following situation progress:
Initial value processing: Xi/xi(1)={ x 'i(k) | k=1,2 ... n, xi}, (1) ≠ 0 i=1,2 ... m；(3)
Data are adopted for accumulation note, because data have apparent increasing trend, then use equalization nondimensionalization processing side
Formula；
Data are adopted for monthly note, because data do not have apparent increasing trend, are then handled using section nondimensionalization
Mode；
Wherein: i is to compare ordered series of numbers type mark number；
K is the ordered series of numbers element numbers after nondimensionalization；
N is element number in ordered series of numbers；
xiIt (k) is k-th of element in original data series；
In actual use, due to the data in factor each in system column may because of dimension difference, be not easy to compare or
It is difficult to obtain correct conclusion when comparing.The use of the technical program easily facilitates subsequent comparison so that dimension is unified.
Example IV:
A kind of oil-water well dynamic connectivity method of discrimination according to figure 1, with embodiment one the difference is that: it is described
The step of four seek weight using entropy weight weighing methods, and then determine that the degree of association is to obtain with the following method:
It is located at m indexs, n to be evaluated in the system of object, iotave evaluation matrix is Dn,m, place is standardized to it
Reason obtains Standard Process Rn,m, according to entropy weight law theory, the entropy of jth item is calculated by following formula
The entropy weight (weight) of jth item is calculated by following formula:
In above formula:fi,jWhen=0, fi,jlnfi,j=0.
Wherein: HjFor the entropy of jth item；
I is to be evaluated object tag number；
N is to be evaluated object total number；
fi,jFor intermediate variable；
ωjFor the entropy weight of jth item.
In actual use, weight can be adaptively determined from the angle of effective information using the technical program, had
Conducive to the stability for improving analysis result.
Embodiment five:
A kind of oil-water well dynamic connectivity method of discrimination according to figure 1, with embodiment one the difference is that: it is described
The step of four in relating value r0,iIt is to be calculated by following formula
Wherein, i=1,2 ... m.
In actual use, the size of the related coefficient of calculating has reacted the degree of correlation of the note amount of adopting, and reflects oil indirectly
Connectivity between well.By the multiple related coefficients of comprehensive analysis, can qualitatively judge between oil-water well whether advantageous channel,
Guidance, preferable adjusting and optimizing injection-production well parameter are provided to next step corrective measure.
The degree of association sorts by size, if r0,i<r0,j, then compare sequence xjWith reference sequences x0Relatively compare sequence xiWith ginseng
Examine sequence x0More like, i.e. the connectivity of j and number output well and No. 0 injection well is better than the connection of i output well and No. 0 injection well
Property.
Embodiment six:
Taking reference sequences is x0=(1,2,3,4,5,6,7,8), compares sequence are as follows: x1=(21,23,22,24,26,25,
27,26,28), x2(3,2,3,4,3,4,6,7,6).Three sequences are as shown in Figure 2:
From the similitude angle of variation tendency, sequence x0It (can be weighed after translation with sequence variation tendency having the same
Close), the two, which is presented, to be positively correlated, degree of correlation x1It is high；Sequence x0With sequence x2Variation tendency difference it is larger, the degree of correlation is lower.
But it is as follows using Deng Shi related degree model calculated result:
γ0,1=0.3333, γ0,2=0.8868,
Show sequence x0With sequence x2It is increasingly similar, it is opposite with the conclusion of Such analysis.This also indicates that Deng Shi related degree model
Be not suitable for adopting data sequence research inter well connectivity based on note.
At the same time, the degree of association result that new model calculates is as follows:
γ0,1=1, γ0,2=-0.1886
Show sequence x0With sequence more x1It is similar, it is consistent with the conclusion of Such analysis.This also indicates that new related degree model
It is suitably based on note and adopts data sequence research inter well connectivity.
Embodiment seven:
Taking reference sequences is x0=(1,2,3,4,5,6,7,8,9), compares sequence are as follows: x1=(9,8,7,6,5,4,3,2,
1), it is clear that reference sequences are in incremental state, and compare sequence and be in the state successively decreased.If regarding reference sequences as note
Enter amount data, sequence will be compared and regard quantum of output data as, then can intuitively find out well corresponding to the two from variation tendency
Between be not present strong connectivity.The following table 1 gives the knot calculated based on Deng Shi related degree model and the new related degree model of this paper
Fruit:
The comparison of 1 calculated result of table
Obviously, Deng Shi association angle value shows that there are stronger association, misleadings easy to form between two sequences.And invent new association
Degree model then points out there is negative correlativity between two sequences, and the increase of injection rate does not increase quantum of output.This shows
For adopting data sequence research inter well connectivity problem based on note, new model is superior compared with Deng Shi related degree model.
Embodiment eight:
An injection-production well group of XX block is chosen, which is adopted by water injection well WJW (for under-injected well) and corresponding two mouthfuls
Well OW-1, OW-2 is formed out, and the relative position of each well is as shown in Figure 3.Record display water injection well WJW belongs to under-injected well.Choosing now
Note during taking in June, -2017 in June, 2015 adopts basic data and carries out grey relational grade analysis.Selected basic data is
Moon water injection rate (the m of injection well3/ d), the moon Liquid output (m of output well3/ d), as shown in table 2.
Basic data (the m in table in June, -2017 in June, 2 20153/d)
Based on above-mentioned data, Deng Shi related degree model and new related degree model of the invention is respectively adopted, calculates injection-production well
Between grey relational grade, as a result as shown in table 3 below:
The calculated value of the different related degree models of table 3
As can be seen from the above table, the value of the Deng Shi degree of association is more than 0.5, and gap very little, shorts situation not with actual
Meet.And the association angle value that related degree model proposed in this paper calculates is all less than normal, prompts the connectivity between injection-production well poor, meets
The actual conditions shorted.In addition, related degree model of the present invention, can distinguish the connectivity of two mouthfuls of corresponding output wells and water injection well
It has a long way to go, the connectivity of OW-1 well and WJW well is better than the connectivity of OW-2 well Yu WJW well.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
In the absence of conflict, those skilled in the art can according to the actual situation will be relevant in above-mentioned each example
Technical characteristic is combined with each other, and to reach corresponding technical effect, will not repeat them here particularly for various combined situations.
The above, only presently preferred embodiments of the present invention, the present invention be not intended to be limited to it is shown in this article this
A little embodiments, and it is to fit to the widest scope consistent with principles disclosed herein and novel features.According to this hair
Bright technical spirit any simple modification, equivalent change and modification to the above embodiments, still fall within the technology of the present invention
In the range of scheme.
Claims (5)
1. a kind of oil-water well dynamic connectivity method of discrimination, which comprises the steps of:
Step 1: the data type that data analysis uses is determined
Determine the essential data that the monthly injection rate of well is analyzed as data；
Step 2: analytical sequence is determined
Select injection well or output well will be another using the centerwell data of concern as reference sequence as the centerwell of concern
Well data are taken as comparing ordered series of numbers；
Y is set as reference sequence, i.e. Y is
Y=y (k) | k=1,2 ... n }；(1)
X is set as comparing ordered series of numbers, i.e. X is
Xi={ xi(k) | k=1,2 ... n }, i=1,2 ... m；(2)
Wherein:
Y (k) is reference sequence data element；
X (k) is to compare ordered series of numbers data element；
K is element numbers in ordered series of numbers；
I is to compare ordered series of numbers type mark number；
Step 3: the nondimensionalization of variable
By the reference sequence that step 2 determines and the data for comparing ordered series of numbers, dimensionless is carried out using equalization or section mode
Change；
Step 4: calculating correlation
Weight is sought using entropy weight weighing method, and then determines the degree of association；
Step 5: relational degree taxis simultaneously judges connectivity
The association angle value that step 4 is calculated carries out size sequence, and the big connectivity with the centerwell of concern of association angle value is better than
It is associated with the small connectivity with the centerwell of concern of angle value.
2. a kind of oil-water well dynamic connectivity method of discrimination as described in claim 1, it is characterised in that: in the step one
Data type further include the monthly Liquid output of oil well, choose in two item data of monthly moisture content it is one or two kinds of.
3. a kind of oil-water well dynamic connectivity method of discrimination as described in claim 1, which is characterized in that in the step three
Nondimensionalization is carried out using equalization or section mode, a point following situation carries out:
Initial value processing: Xi/xi(1)={ xi' (k) | k=1,2 ... n, xi}, (1) ≠ 0 i=1,2 ... m；(3)
Data are adopted for accumulation note, because data have apparent increasing trend, then use equalization nondimensionalization processing mode；
Data are adopted for monthly note, because data do not have apparent increasing trend, then use section nondimensionalization processing mode；
Wherein: i is to compare ordered series of numbers type mark number；
K is the ordered series of numbers element numbers after nondimensionalization；
N is element number in ordered series of numbers；
xiIt (k) is k-th of element in original data series.
4. a kind of oil-water well dynamic connectivity method of discrimination as described in claim 1, which is characterized in that the step four is adopted
Weight is sought with entropy weight weighing method, and then determines that the degree of association is to obtain with the following method:
It is located at m indexs, n to be evaluated in the system of object, iotave evaluation matrix is Dn,m, it is standardized
To Standard Process Rn,m, according to entropy weight law theory, the entropy of jth item is calculated by following formula
The entropy weight (weight) of jth item is calculated by following formula:
In above formula:fi,jWhen=0, fi,jln fi,j=0；
Wherein: HjFor the entropy of jth item；
I is to be evaluated object tag number；
N is to be evaluated object total number；
fi,jFor intermediate variable；
ωjFor the entropy weight of jth item.
5. a kind of oil-water well dynamic connectivity method of discrimination as described in claim 1, it is characterised in that: in the step four
Relating value r0,iIt is to be calculated by following formula
Wherein, i=1,2 ... m.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112901145A (en) * | 2021-03-19 | 2021-06-04 | 大庆油田有限责任公司 | Volume energy method for analyzing injection-production relation between oil-water wells |
| CN113177319A (en) * | 2021-04-30 | 2021-07-27 | 中国石油大学(华东) | Method and system for judging connectivity among wells based on neural network sensitivity analysis |
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